SoroushVahidi/scidocs huggingface dataset:
A First Look at Android Malware Traffic in First Few Minutes
(2015)

With the advent of mobile era, mobile terminals are going through a trend of surpassing PC to become the most popular computing device. Meanwhile, the hackers and viruswriters are paying close attention to the mobile terminals, especially the Android platform. The growing of malwares on the Android system has drawn attentions from both the academia and security industry. Recently, mobile network traffic analysis has been used to identify the malware. But due to the lack of a large-scale malware repository and a systematic analysis of network traffic features, the existing research mostly remain in theory. In this paper, we design an Android malware traffic behavior monitoring scheme to capture traffic data generated by malware samples in a real Internet environment. We capture the network traffic from 5560 malware samples in the first 5 minutes, and analyze the major compositions of the traffic data. We discover that HTTP and DNS traffic are accounted for more than 99% on the application layer traffic. We then present an analysis of related network features: DNS query, HTTP packet length, ratio of downlink to uplink traffic amount, HTTP request and Ad traffic feature. Our statistical results illustrate that: (1) more than 70% malwares generate malicious traffic in the first 5 minutes, (2) DNS query and HTTP request can be used to identify the malware, and the detection rate reaches 69.55% and 40.89% respectively, (3) Ad traffic can greatly affect the malware detection. We believe our research provides an in-depth analysis into mobile malwares' network behaviors.
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
(2017)

We propose a method for embedding twodimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.
Restoration of Endodontically Treated Molars Using All Ceramic Endocrowns
(2013)

Clinical success of endodontically treated posterior teeth is determined by the postendodontic restoration. Several options have been proposed to restore endodontically treated teeth. Endocrowns represent a conservative and esthetic restorative alternative to full coverage crowns. The preparation consists of a circular equigingival butt-joint margin and central retention cavity into the entire pulp chamber constructing both the crown and the core as a single unit. The case reports discussed here are moderately damaged endodontically treated molars restored using all ceramic endocrowns fabricated using two different systems, namely, CAD/CAM and pressed ceramic.
Efficient lineage tracking for scientific workflows
(2008)

Data lineage and data provenance are key to the management of scientific data. Not knowing the exact provenance and processing pipeline used to produce a derived data set often renders the data set useless from a scientific point of view. On the positive side, capturing provenance information is facilitated by the widespread use of workflow tools for processing scientific data. The workflow process describes all the steps involved in producing a given data set and, hence, captures its lineage. On the negative side, efficiently storing and querying workflow based data lineage is not trivial. All existing solutions use recursive queries and even recursive tables to represent the workflows. Such solutions do not scale and are rather inefficient. In this paper we propose an alternative approach to storing lineage information captured as a workflow process. We use a space and query efficient interval representation for dependency graphs and show how to transform arbitrary workflow processes into graphs that can be stored using such representation. We also characterize the problem in terms of its overall complexity and provide a comprehensive performance evaluation of the approach.
A novel tri-state boost PFC converter with fast dynamic performance
(2010)

Dynamic response of boost power factor correction (PFC) converter operating in continuous-conduction mode (CCM) is heavily influenced by low-bandwidth of voltage control loop. A novel tri-state boost PFC converter operating in pseudo-continuous-conduction mode (PCCM) is proposed in this paper. An additional degree of control-freedom introduced by a freewheeling switching control interval helps to achieve PFC control. A simple and fast voltage control loop can be used to maintain a constant output voltage. Furthermore, compared with boost PFC converter operating in conventional discontinuous-conduction mode (DCM), boost PFC converter operating in PCCM demonstrates greatly improved current handling capability with reduced current and voltage ripples. Analytical and simulation results of the tri-state boost PFC converter have been presented and compared with those of the boost PFC converter operating in conventional CCM and DCM. Simulation results show excellent dynamic performance of the tri-state boost PFC converter.
A NoSQL Data Model for Scalable Big Data Workflow Execution
(2016)

While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.
Torque ripple improvement for synchronous reluctance motor using an asymmetric flux barrier arrangement
(2004)

An interior permanent-magnet synchronous motor (IPMSM) is a highly efficient motor and operates in a wide speed range; therefore, it is used in many industrial and home appliance applications. However, the torque ripple of synchronous motors such as the IPMSM and synchronous reluctance motor is very large. The variation of magnetic resistance between the flux barriers and teeth causes the torque ripple. In this paper, flux barriers are asymmetrically designed so that the relative positions between the outer edges of the flux barriers and the teeth do not correspond. As a result, torque ripple can be reduced dramatically.
Analysis of Rotor Core Eddy-Current Losses in Interior Permanent-Magnet Synchronous Machines
(2008)

This paper presents the results of an investigation focused on the rotor core eddy-current losses of interior permanent-magnet (IPM) synchronous machines. First, analytical insight into the rotor core eddy-current losses of IPM machines is developed. Next, major design parameters that have the most significant impact on the rotor core eddy-current losses of IPM machines are identified. Finite-element analysis results are then presented to compare the predicted eddy-current losses in the machine core of IPM machines with one- and two-layer rotors coupled with concentrated- and distributed-winding stators. It is shown that the lowest total eddy-current losses in the machine core are achieved using a combination of distributed stator windings and two magnet layers per rotor pole, whereas minimizing only the rotor core eddy-current losses favors replacement of the rotor with a single-layer configuration.
Optimization of average and cogging torque in 3-phase IPM motor drives
(2002)

In this paper, an interior permanent magnet (IPM) brushless DC motor for traction applications is analyzed. The effect of magnetization direction, number of stator slots and current waveform on torque pulsation are examined. A three-phase, four-pole IPM motor is considered for the study. The finite element method is used to calculate the torque, reluctance torque, back iron flux density, tooth flux density, detent torque and back-EMF of the motor. It is shown that because of the reluctance torque resulting from rotor saliency, the peak point in the torque-angle curve is shifted to the left. Therefore, it is not possible to find the switching instants just by considering the direction of stator and rotor MMF and keeping the right angle between them. A Matlab program has been developed to rind the switching intervals, which will produce the maximum average torque and minimum cogging torque. Experimental results to support the simulation findings are included in the paper.
Iron loss analysis of interior permanent-magnet synchronous motors-variation of main loss factors due to driving condition
(2005)

In this paper, the authors investigate the iron loss of interior permanent magnet motors driven by pulsewidth modulation (PWM) inverters from both results of the experiments and the finite-element analysis. In the analysis, the iron loss of the motor is decomposed into several components due to their origins, for instance, the fundamental field, carrier of the PWM inverter, slot ripples, and harmonic magnetomotive forces of the permanent magnet in order to clarify the main loss factors. The Fourier transformation and the finite-element method considering the carrier harmonics are applied to this calculation. The calculated iron loss is compared with the measurement at each driving condition. The measured and the calculated results agree well. It is clarified that the iron loss caused by the carrier of the PWM inverter is the largest component at low-speed condition under the maximum torque control, whereas the loss caused by the harmonic magnetomotive forces of the permanent magnet remarkably increase at high-speed condition under the flux-weakening control
A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm
(2007)

Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods
Articulatory and spectrum features integration using generalized distillation framework
(2016)

It has been shown that by combining the acoustic and articulatory information significant performance improvements in automatic speech recognition (ASR) task can be achieved. In practice, however, articulatory information is not available during recognition and the general approach is to estimate it from the acoustic signal. In this paper, we propose a different approach based on the generalized distillation framework, where acoustic-articulatory inversion is not necessary. We trained two DNN models: one called “teacher” learns from both acoustic and articulatory features and the other one called “student” is trained on acoustic features only, but its training process is guided by the “teacher” model and can reach a better performance that can't be obtained by regular training even without articulatory feature inputs during test time. The paper is organized as follows: Section 1 gives the introduction and briefly discusses some related works. Section 2 describes the distillation training process, Section 3 describes ASR system used in this paper. Section 4 presents the experiments and the paper is concluded by Section 5.
Invertible Conditional GANs for image editing
(2016)

Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.
Machine Learning Approaches for Mood Classification of Songs toward Music Search Engine
(2009)

Human often wants to listen to music that fits best his current emotion. A grasp of emotions in songs might be a great help for us to effectively discover music. In this paper, we aimed at automatically classifying moods of songs based on lyrics and metadata, and proposed several methods for supervised learning of classifiers. In future, we plan to use automatically identified moods of songs as metadata in our music search engine. Mood categories in a famous contest about Audio Music Mood Classification (MIREX 2007) are applied for our system. The training data is collected from a LiveJournal blog site in which each blog entry is tagged with a mood and a song. Then three kinds of machine learning algorithms are applied for training classifiers: SVM, Naive Bayes and Graph-based methods. The experiments showed that artist, sentiment words, putting more weight for words in chorus and title parts are effective for mood classification. Graph-based method promises a good improvement if we have rich relationship information among songs.
A Study on the Customer-Based Brand Equity of Online Retailers
(2010)

Different from traditional firms, online retailers do not often produce common goods on their own. On the contrary, they sell merchandises that traditional firms produce. Furthermore, online retailers are also different from traditional retailers because the former do not often have frontline employees, physical outlets, or displaying layouts. Therefore, when building a strong online retailer brand, it is not enough to only consider the traditional dimensions of customer-based brand equity, namely brand awareness, perceived quality, brand associations, and brand loyalty. Some dimensions, i.e. trust associations and emotional connection need to be distinguished or included in the context of online business. Besides, the willingness to pay a price premium is always being viewed as a good indicator to measure brand equity, but for online retailers, it is conditional on some premises. By structural equation modeling, it was verified that for online retailers, the importance of brand awareness and trust associations decreased but perceived quality remained its strong influence. Brand loyalty was the better indicator for measuring brand equity, and emotional connection became so critical that if without it, customers would lack willingness to pay a price premium.
Automatic Fingerprint Recognition Systems
(2004)

This article summarizes the major developments in the history of efforts to use fingerprint patterns to identify individuals, from the earliest fingerprint classification systems of Vucetich and Henry in the 1890s through the advent of automated fingerprint identification. By chronicling the history of “manual” systems for recording storing, matching, and retrieving fingerprints, the article puts advances in automatic fingerprint recognition in historical context and highlights their historical and social significance.
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
(2015)

Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural network or long short-term memory. Afterwards, semantics of sentences and their relations are adaptively encoded in document representation with gated recurrent neural network. We conduct document level sentiment classification on four large-scale review datasets from IMDB and Yelp Dataset Challenge. Experimental results show that: (1) our neural model shows superior performances over several state-of-the-art algorithms; (2) gated recurrent neural network dramatically outperforms standard recurrent neural network in document modeling for sentiment classification.1
Re-embedding words
(2013)

We present a fast method for re-purposing existing semantic word vectors to improve performance in a supervised task. Recently, with an increase in computing resources, it became possible to learn rich word embeddings from massive amounts of unlabeled data. However, some methods take days or weeks to learn good embeddings, and some are notoriously difficult to train. We propose a method that takes as input an existing embedding, some labeled data, and produces an embedding in the same space, but with a better predictive performance in the supervised task. We show improvement on the task of sentiment classification with respect to several baselines, and observe that the approach is most useful when the training set is sufficiently small.
Reference table based k-anonymous private blocking
(2012)

Privacy Preserving Record Linkage is an emerging field of research which attempts to deal with the classical linkage problem from a privacy preserving point of view. In this paper we propose a novel approach for performing Privacy Preserving Blocking in order to minimize the computational cost of Privacy Preserving Record Linkage. We achieve this without compromising privacy by using Nearest Neighbors clustering, a well-known clustering algorithm and by using a reference table. A reference table is a publicly known table the contents of which are used as intermediate references. The combination of Nearest Neighbors and a reference table offers our approach k-anonymity characteristics.
Pedestrian Behaviour Monitoring: Methods and Experiences
(2009)

The investigation of pedestrian spatio-temporal behaviour is of particular interest in many different research fields. Disciplines like travel behaviour research and tourism research, social sciences, artificial intelligence, geoinformation and many others have approached this subject from different perspectives. Depending on the particular research questions, various methods of data collection and analysis have been developed and applied in order to gain insight into specific aspects of human motion behaviour and the determinants influencing spatial activities. In this contribution, we provide a general overview about most commonly used methods for monitoring and analysing human spatio-temporal behaviour. After discussing frequently used empirical methods of data collection and emphasising related advantages and limitations, we present seven case studies concerning the collection and analysis of human motion behaviour following different purposes.
ANALYSIS OF SEGMENTATION PARAMETERS IN ECOGNITION SOFTWARE USING HIGH RESOLUTION QUICKBIRD MS IMAGERY
(2006)

For objectoriented classification approaches, main step is the segmentation part of the imagery. In eCognition v.4.0.6 software, with the segmentation process, meaningful objects can be created for following steps. In the experimental imagery with 2.4m ground sampling distance (GSD) has been used and several different parameters e.g. scale, color/shape and smoothness/compactness parameters have been tested accordingly. Additionally, segmentation parameters were set to low and high values and thus, dissimilarity of segmentation results were examined.